License, removed tools/

Author: chrischoy

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2016 Christopher B. Choy
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,6 +1,6 @@
 # 3D-R<sup>2</sup>N<sup>2</sup>: 3D Recurrent Reconstruction Neural Network
 
-This repository contains the source codes for the paper [Choy et al., 3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction, ECCV 2016](http://arxiv.org/abs/1604.00449). Given one or multiple views of an object, the network generates voxelized (voxel is 3D equivalent of pixel) reconstruction of the object in 3D. This is the first work that generates a voxelized 3D reconstruction given 2D image observations using a neural network.
+This repository contains the source codes for the paper [Choy et al., 3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction, ECCV 2016](http://arxiv.org/abs/1604.00449). Given one or multiple views of an object, the network generates voxelized (voxel is 3D equivalent of pixel) reconstruction of the object in 3D. This is the first work that generates a voxelized 3D reconstruction given multiple 2D image observations using a recurrent neural network.
 
 ## Citing this work
 
@@ -22,9 +22,9 @@ If you find this work useful in your research, please consider citing:
 
 Traditionally, single view reconstruction and multi view reconstruction are disjoint problmes that has been dealt using different approaches. In this work, we first propose a unified framework for both single and multi view reconstruction using a `3D Recurrent Reconstruction Neural Network` (3D-R2N2).
 
-| Schematic of the `3D-Convolutional LSTM` | Inputs (red cells + feature) for each cell (purple) |
-|:----------------------------------------:|:---------------------------------------------------:|
-| ![3D-LSTM](imgs/lstm.png)                | ![3D-LSTM](imgs/lstm_time.png)                      |
+| The schematic of the `3D-Convolutional LSTM` | Inputs (red cells + feature) for each cell (purple) |
+|:--------------------------------------------:|:---------------------------------------------------:|
+| ![3D-LSTM](imgs/lstm.png)                    | ![3D-LSTM](imgs/lstm_time.png)                      |
 
 We can feed in images a random order since the network is trained to be invariant to the order. The ciritical component that enables the network to be invariant to the order is the `3D-Convolutional LSTM` which we first proposed in this work. The `3D-Convolutional LSTM` selectively updates parts that are visible and keeps the parts that are self occluded (please refer to [http://cvgl.stanford.edu/3d-r2n2/](http://cvgl.stanford.edu/3d-r2n2/) for the supplementary material for analysis).
 
@@ -91,3 +91,7 @@ tar -xzf ShapeNetVox32.tgz -C ShapeNet/
 ```
 ./experiments/script/res_gru_net.sh
 ```
+
+## LICENSE
+
+MIT License

tools/binvox_rw.py lib/binvox_rw.py

@@ -15,8 +15,7 @@
 from lib.config import cfg
 from lib.data_augmentation import preprocess_img
 from lib.data_io import get_voxel_file, get_rendering_file
-
-import tools.binvox_rw as binvox_rw
+from lib.binvox_rw import read_as_3d_array
 
 
 def print_error(func):
@@ -171,7 +170,7 @@ def load_img(self, category, model_id, image_id):
     def load_label(self, category, model_id):
         voxel_fn = get_voxel_file(category, model_id)
         with open(voxel_fn, 'rb') as f:
-            voxel = binvox_rw.read_as_3d_array(f)
+            voxel = read_as_3d_array(f)
 
         return voxel